Detecting occupancy behaviour of buildings through environment monitoring sensing

The goal of this research is to produce a realistic occupancy forecast as well as an ideal occupancy-based controller for increasing the performance of Heating Ventilation and Air-Conditioning [HVAC] systems. Reliable occupancy prediction is a critical enabler for demand-based HVAC control since it ensures that the HVAC system is not running unnecessarily while a room or zone is empty. We present simple yet successful occupancy prediction methods, as well as an approach for automatically setting temperature set-points, in this work. We present three alternative occupancy prediction algorithms based on historical occupancy measurements. The proposed approach uses historical occupancy profile and environmental room data to establish an occupancy identity strategy that can provide a stochastic model based on uncertain basis functions as an alternative. According to the findings, the proposed occupancy prediction algorithms could reach an accuracy of approximately 85% reliable prediction with few negative predictions.